Clinical trials subject identification system

ABSTRACT

Matching a subject with a clinical trial includes steps of: collecting patient data associated with the subject; collecting clinical trial data from multiple sources; matching the subject to a clinical trial scheduled in a location accessible to the subject; notifying a health care provider associated with the subject about the clinical trial; and receiving a response.

CROSS-REFERENCE TO RELATED APPLICATIONS

None.

STATEMENT REGARDING FEDERALLY SPONSORED-RESEARCH OR DEVELOPMENT

None.

INCORPORATION BY REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

None.

FIELD OF THE INVENTION

The invention disclosed broadly relates to the field of medicalinformation technology, and more particularly relates to the field ofinformation technology related to clinical trials.

BACKGROUND OF THE INVENTION

A clinical trial is a medical research study involving human subjects.Clinical trials are used to determine if a new medical drug (compound),medical device, or medical procedure is safe and effective for humanuse. Clinical trials are scheduled after successful pre-clinical animaltesting. Clinical trials are burdened with expense and inefficiency inthe recruitment of subjects. The average number of eligibility criteriaused to screen volunteers has risen exponentially over the years,contributing to a decline in volunteers willing to enroll in the trials.

Additionally, the number of required procedures for each clinical trialhas risen dramatically in the last several years. This is partly becauseof the increasing complexity of the studies themselves, and partlybecause of the mandated safeguards. The increased procedures dissuadevolunteers from completing the trials. Because clinical trials involveexperimental drugs and procedures, a host of safeguards has been set inplace to protect the human subject as much as possible. The Departmentof Health and Human Services mandates policies to protect theconfidentiality and rights of human trial subjects. Standards andguidelines are in place to ensure that the final results that arereported are accurate and credible and that the confidentiality andrights of trial participants are protected. While necessary, this adds atremendous administrative overhead to an already expensive endeavor.

There is a need for a method to improve the efficiency while reducingthe costs of clinical trials.

SUMMARY OF THE INVENTION

Briefly, according to an embodiment of the invention a method ofautomatically matching a subject with an available clinical trialsincludes steps or acts of: collecting patient data associated with thesubject; collecting clinical trial data from multiple sources; matchingthe subject to a clinical trial scheduled in a location accessible tothe subject; notifying a health care provider associated with thesubject about the clinical trial; and receiving a response.

According to another embodiment of the present invention, a method ofsoliciting a subject for a clinical trial includes steps or acts ofreceiving a health-related search term provided by the subject;determining a location of the subject; matching the health-relatedsearch term to a target keyword from a data store; mapping the targetkeyword to a medical condition under consideration for a clinical trial;determining that the clinical trial for the medical condition isscheduled in a location accessible to the subject; and serving anannouncement to the subject with links to a site with information aboutthe clinical trial.

According to another embodiment of the present invention, a distributedcomputing system for identifying a subject for a clinical trialincludes: a memory with computer-executable instructions stored therein,and a processor device operably coupled with the memory. Thecomputer-executable instructions cause a computer to perform: collectingpatient data associated with the subject; collecting clinical trial datafrom multiple sources; matching the subject to a clinical trialscheduled in a location accessible to the subject; notifying a healthcare provider associated with the subject about the clinical trial; andreceiving a response.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To describe the foregoing and other exemplary purposes, aspects, andadvantages, we use the following detailed description of an exemplaryembodiment of the invention with reference to the drawings, in which:

FIG. 1 is a high level block diagram showing an information processingsystem configured to operate according to an embodiment of the presentinvention;

FIG. 2 is a flowchart of a method according to an embodiment of theinvention;

FIG. 3 is a flowchart of a method according to another embodiment of thepresent invention;

FIG. 4 shows an exemplary method of soliciting subjects for clinicaltrials, according to an embodiment of the present invention;

FIG. 5 is a high level block diagram showing an information processingsystem configured to operate according to an embodiment of the presentinvention; and

FIG. 6 is a flowchart of the method for matching search terms toclinical trials, according to an embodiment of the present invention;

FIG. 7 is an exemplary look-up table, according to an embodiment of thepresent invention;

FIG. 8 depicts exemplary look-up tables matching a subject with a trial,according to an embodiment of the present invention;

FIG. 9 is a simplified depiction of the simulator, according to anembodiment of the present invention;

FIG. 10 is a simplified block diagram of a survey generator, accordingto an embodiment of the present invention;

FIG. 11 shows a simplified depiction of an on-line registration form,according to an embodiment of the present invention; and

FIG. 12 shows an exemplary illustration of a manner in which clinicaltrial subjects can be identified, according to an embodiment of thepresent invention.

While the invention as claimed can be modified into alternative forms,specific embodiments thereof are shown by way of example in the drawingsand will herein be described in detail. It should be understood,however, that the drawings and detailed description thereto are notintended to limit the invention to the particular form disclosed, but onthe contrary, the intention is to cover all modifications, equivalentsand alternatives falling within the scope of the present invention.

DETAILED DESCRIPTION

Before describing in detail embodiments that are in accordance with thepresent invention, it should be observed that the embodiments resideprimarily in combinations of method steps and system components relatedto systems and methods for placing computation inside a communicationnetwork. Accordingly, the system components and method steps have beenrepresented where appropriate by conventional symbols in the drawings,showing only those specific details that are pertinent to understandingthe embodiments of the present invention so as not to obscure thedisclosure with details that will be readily apparent to those ofordinary skill in the art having the benefit of the description herein.Thus, it will be appreciated that for simplicity and clarity ofillustration, common and well-understood elements that are useful ornecessary in a commercially feasible embodiment may not be depicted inorder to facilitate a less obstructed view of these various embodiments.

We describe an automated clinical trials subject identification systemto match subjects with clinical trials. The net effect of this inventionis an increased amount of pre-qualified subjects for clinical trials,and a reduction in recruitment costs. This enlarges the statisticalbase, a requirement for management of population studies. We alsoaccelerate the path to discovery and market, representing millions ofdollars per week to pharmaceutical companies.

The present invention will now be described with respect to FIGS. 1through 12, which are block diagrams and flowchart illustrations ofembodiments of the present invention. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions residingin a computer component.

Referring now to the drawings and to FIG. 1 in particular, there isillustrated an automated clinical trials subject identification system100, according to an embodiment of the present invention. The system 100receives input from several sources. One of these sources representspatients who might become subjects for clinical trials. The patients arefrom geographically distributed regions. This is important for mostclinical trials because it assures a better random sampling of thepopulation as a whole.

In one embodiment of the present invention, the patients contact theirregional Medical Call Center 120 via phone, Web chat, messaging, and thelike, to report on a variety of health conditions. This information caninclude: a) current and past medical conditions; b) emergencies; c)requests for call backs; d) health questions; and e) medicationinformation. Additionally the patients can provide valuable informationabout lifestyle, genomics, and environmental factors that add anotherdimension to their health profile.

By partnering with Call Centers, 120 we leverage the “tech support”paradigm of remote data/voice/video convergence to provide a powerfulintegrated voice/data/video platform. Patients can access the CallCenters 120 by telephone or through their Web browser using a“click-to-talk” or “chat” feature provided on a website. If a patient issearching the Web seeking information about a health concern, thepatient is able to click on an icon or hyperlink on the browser to speakwith a call center agent. With this integrated Web search and talkfeature, the patient is able to speak with a qualified agent liveon-line, without having to go off-line to use the telephone.

The Call Center Database Servers 120 capture the patient data 110 fromthe voice transaction between the patients and call center staff. Thecall center 120 agent can “recognize” the caller by a cookie file (or byuser login) and see the Web page the user is calling from. This allowsthe call center 120 agent to intelligently and expeditiously route thecall.

We permanently and securely store the data 110 derived fromcommunications with patients in a comprehensive, structured, searchablecomputer system readable format in the Call Center 120 servers. Weprovide Health Insurance Portability and Accountability Act (HIPAA)compliance, which is currently mandatory in the management of patienthealth information. The Call Center servers 120 act as an ElectronicHealth Record for the patients and the Call Center 120 personnel areable to provide continuity of communications by viewing history andprofile information. Additionally, Data Encryption 125 provides secureand HIPAA-compliant electronic information transfers between the CallCenter Database Servers 120 and related recipients of information.

The Adapter 130 is operably coupled with the Call Center 120 servers toprovide the electronic interface, communications protocol, andmanagement of information transfers to and from the Call Center Servers120 and the Hub 150. This provides the governance, security, andintelligence of data transfers for integration, synthesis and analysis.The Adapter 130 combines several data sources on-site automatically andsecurely transmits patient details to the data hub centers 150.

The Hub 150 is operably coupled with the Adapter 130. The Hub 150 isanalogous to the central processing unit within a computer. It receivescontinuous feeds of electronic data from the Call Center Databases 120and from the Clinical Trials Web sites monitoring database 160. The Hub150 provides the intelligent matching of subject to clinical trial. Withthe attributes of an electronic learning system, the Hub 150 refines itssearch and matching with each transaction. Algorithms are used as thebasis for the matching engine; these provide the rules and criteria forinformation selection and processing.

We provide here exemplary algorithms for zip code proximity search ontrials and patients, practices:

D=R*(arcos(cos(O1)*(os(O2)*(LatLon2−LatLon1)+sin(O1)*sin(O2),

where Longitudes and Latitudes are pre-calculated on look-ups of zipcode.

To determine the “value” of various inclusionary and exclusionaryfactors in the trials data we use a technique called “Weighted SampleVariance” represented by:

$\sigma^{2} = \frac{\sum\limits_{i = 1}^{N}\left( {x_{i} - \mu} \right)^{2}}{N}$$\sigma_{weighted}^{2} = \frac{\sum\limits_{i = 1}^{N}{w_{i}\left( {x_{i} - \mu^{*}} \right)}^{2}}{V_{1}}$where${V_{1} = {\sum\limits_{i = 1}^{n}w_{i}}},{{which}\mspace{14mu} {is}\mspace{14mu} 1\mspace{14mu} {for}\mspace{14mu} {normalized}\mspace{14mu} {{weights}.}}$

With the following for specific vectors.

W _(i)=Σ_(i) ⁻¹.

The weighted mean in this case is:

${\overset{\_}{x} = {\left( {\sum\limits_{i = 1}^{n}\sum\limits_{i}^{- 1}} \right)^{- 1}\left( {\sum\limits_{i = 1}^{n}{\sum\limits_{i}^{- 1}x_{i}}} \right)}},$

and the covariance of the weighted mean is:

${\sum\limits_{\overset{\_}{x}}{= \left( {\sum\limits_{i = 1}^{n}\sum\limits_{i}^{- 1}} \right)^{- 1}}},$

Secure Data Relays 152 enable the private and HIPAA compliance ofsensitive data transfers to the Hub 150. This is achieved through thecombination of secure networks, software security technologies, andprocess management by the communications services provider. All datacollected from partner Call Centers 120 are securely sent to our dataprocessing Hub 150 for matching and notification. All patient andclinical trial matches are stored in the Hub data store 156.

Firewalls 154 (electronic data firewalls) are used to protect, isolate,and manage traffic in and out of the Hub 150. These firewalls 154 useestablished protocols and security techniques to ensure the authenticityand safety of data.

From the Hub 150, notifications are generated for the patient's healthcare providers (HCP) indicating a potential qualification for a clinicaltrial, and could be relayed to patients by their HCP. Notifications areprovided by computer generated means, but they can also be relayed byfax, letter, or phone, if need be. The latter methods of transmissionmay be necessary for the elderly patients. HCP in this context refers tothe individual or group responsible for monitoring the health of thepatient. Traditionally the HCP was the patient's physician. As healthcare evolves, the HCP today can be a medical doctor, a physician'sassistant, a hospitalist, a doctor of osteopathy, a medical center,clinic, or other. A new direction in medical care is the role of HCPsproviding the “Medical Home” for the patient. The Medical Home, in thiscase, is the integrated health team.

Individual profiles for patients, health care providers, and clinicaltrial organizations can be developed from here for selecting health careproviders most likely to refer, patients most interested in clinicaltrials, and clinical trial organizations and pharmaceutical companiesthat will want to refine and perfect the desired population set based onthe intelligence of the Hub 150. Hub 150 communications occur within thesafety of the electronic firewall 154. All communications between theHub engine 150 and its databases 156 are secure and HIPAA Compliant.

The Hub Database 156 stores all matches by the Hub engine 150. Theserecords are used for the management of confidentiality, historicalresponsibility, and developing further intelligence in the Hub's 150algorithms and filters.

Data Feeds from the published Clinical Trials databases 160 areelectronic links to publicly available Web sites. These sites define:criteria, timing, logistics, and geographical considerations for currentand future clinical trials. Data Feeds from our proprietary ClinicalTrials Database 160 provide the Hub 150 with an internal and proprietarysource of information from an integrated Clinical Trials database 160.

The Clinical Trials Database 160 manages the proper selection ofClinical Trials, their information and the coordination of updates andnotifications. It provides the Hub 150 with a unique intelligence as itmonitors the criteria for thousands of Clinical Trials and can derivepatterns and insight.

The Platform Web Applications 170 perform pre-processing of informationand quality checks prior to notifying a HCP of a potential patient/trialmatch. This technology also structures the information into adeliverable format to the physician or other stakeholder. We pre-processall potential candidate subject/trial matches before they move to theHCPs for patient notifications. The pre-processing step involvesemploying inclusionary and exclusionary factor algorithms to determinewhat gets filtered out. Preprocessing and Quality Checks 170 are doneprior to any information deliveries.

This is done with a combination of software tools. Examples of algorithmcriteria used for filtering include: propensity of subject to enrollbased on history of participation, clinical matching, geographicalimplications (there may be population studies of a specific region).These filters will also have the capability to identify trends in thepopulation by the information being reported and captured. As an exampleof how this would work, a virus such as H1N1 (swine flu) could bedetected across the map and also mapped to areas of highestconcentration or geographical progression. There is a spatial-temporalaspect to this data as it is time stamped and geographically coded. Thisallows for historical reviews of episodes and trends as well asgeographic and demographic perspectives. It can also be used to createstatistically valid clinical demographics.

FIG. 12 shows an illustrative example of one method for identifyingsubjects for participating in clinical trials, according to oneembodiment of the present invention. We perform a search on a HCP'sElectronic Health Records Database 195 to extract patient records 1210(subject to HIPAA rules). Then we map the patient records 1210 from theHCP database 195 to any clinical trials from the Clinical TrialsDatabase 160 to match a patient record 1210 to a listed current orfuture clinical trial. In this example shown in FIG. 12, we find a match1250 with a patient record 1210 for J. Doe from New Orleans, La. to aclinical trial for a diabetes drug scheduled for May in New Orleans. Thematching can be performed using known methods, such as keyword match.

Electronic Mail Alerts 180 are generated for delivery in a number ofsecure and HIPAA compliant methods maintaining the patient-doctorconfidentiality. These are sent directly to the HCP whose responsibilityit is to notify, or not, their patient of a potential clinical trialthat would be of benefit to them. Email alerts 180 go out to patient'sHCPs after pre-validation from our team. The platform will supportemail, SMS (Short Message Service) and third-party software integration,among others, for match notifications. An API (application programinterface) is also provided for Data Sharing services.

The Patient/Health Care Provider Feedback Loop 190 is a key aspect ofthe adaptive property of the invention. Within this loop 190, the HCP“meets” (in person, within chat room, or by video) with the patient todiscuss the ‘matched trial’ and interest to proceed or not. If there isinterest, the HCP provides the supporting information and instructionson how to register. A unique identifier is assigned to the subject inorder to track the subject through the trial and beyond for propermanagement, compensation, and follow-up. The subject reviews theclinical trial details and determines the next step, perhaps contactingthe Clinical Trial Organization to enroll. The subject is encouraged toprovide feedback which is routed back into the Hub 150 for use inrefining the selection and administration process.

Referring now to FIG. 2, we show a high-level flowchart 200 of a methodfor identifying subjects for clinical trials, according to an embodimentof the present invention. The method begins at step 210 with thecollection of patient data 110. We collect patient data 110 frommultiple sources and in varying formats. For example, patient data 110may be input via voice data from partner Call Centers 120 as previouslydiscussed. A partner Call Center 120 receives patient data 110 from asubject contacting the Call Center 120 to request medical adviceregarding a health concern. The patient data 110 must be encrypted toensure privacy.

In step 220 we continuously collect data pertaining to clinical trials,simultaneously with the patient data 110. This data is available fromclinical trial organizations such as http://www.clinicaltrials.gov inthe form of data feeds. It is important to keep up to date withgovernment safeguards and protocols.

In step 230 we perform pre-processing 170 of the match. This includesfeeding the potential match data into an algorithm and then employing afilter tool to further refine the selections. We first determine aplurality of parameters to consider for both the subject and theclinical trial. We assign weighted values to the parameters with ahigher weight given to those parameters we determine are most important;likewise we assign a lower weight to those parameters we feel are lessimportant in determining the likelihood of a match. We derive theimportance of a factor based on what we have learned from our trainingdata. For example, assume we have learned that younger patients in theUnited States are more likely to participate in clinical trials and thatpayment is a great inducement to them. We therefore assign a higherweight to a patient aged under thirty and we also assign a higher weightto a clinical trial that pays its subjects. Another example is: apatient with repetitive visits or communications with their oncologistwould be securely identified to their HCP as a potential candidate foran existing trial.

In step 240 we store the match data in the Hub database 156. The matchdata not only provides the information we need to match potentialsubjects with trials, but it also provides a learning platform. Weconstantly refine and update the match pre-selection process by feedingmatch data as training data into a learning tool.

In step 250 we transmit the match data to the HCP through securechannels. We can transmit the data by email alerts or any other securetransmission format.

In step 260 we implement the feedback loop 190. We ask the HCP toprovide feedback about the patient's decision and any otherconcerns/issues that need to be addressed. The HCP discusses theclinical trial with the subject and receives an indication from thesubject about the subject's inclination to participate. If the subjectdecides to move forward and authorizes contact, we provide the clinicaltrial information, which includes a survey about the process.

Up to this point, we have discussed one method of identifying subjectsfor clinical trials. In another embodiment of the present invention, weactively solicit subjects. Referring now to FIG. 3, we provide anexemplary method of soliciting subjects for clinical trials. With theimmense amount of information available on the Web today, and thefrustrating inefficiencies in health care, it is no wonder that peopleoften turn to the Internet to answer their health questions. Web sitessuch as www.WebMD.com are popular because they are easy to navigate andprovide up-to-date generalized information on a vast array of healthissues. A subject has only to access the Internet through a Web browserand type in a search term. For this example, we assume that the subjecthas input a search query of “high blood sugar” into a search engine.Through a partnership with a web service provider (such as WebMD), weare able to receive this query term. This opens a channel for automaticlinking and proactive advertizing of the service.

In step 310 we determine if the input query term matches our pre-definedtarget keywords, such as “blood sugar,” “high blood sugar,” “diabetes,”and the like. If there is a match to a target keyword, we query alook-up table to map the target keyword with a listing of diseases andother medical conditions. In this case, the look-up table maps thetarget keyword term “blood sugar” to the disease “diabetes” from ourlook-up table (shown in FIG. 7). In FIG. 6 we elaborate on this step.

In step 320 we determine the subject's location. This is accomplishedthrough known geographical identifiers such as cookies and Web siteregistration profiles, phone numbers, and the like. Once the subject'slocation is known, in step 330 we are able to determine if there are anyclinical trials related to the selected medical condition (diabetes)within a reasonable commute of the subject's location. We can establisha threshold distance from the subject's location. Any trials beyond thatthreshold distance are not considered “local.”

If it is determined that local clinical trials are available, then instep 340 we render and serve the target ad to the subject in the form ofa window, box, page, or banner. By “available” we mean a trial that isstill open for registration or that is scheduled to occur within thenear future. Referring now to the example shown in FIG. 4, the ad 410may be served directly onto the search results page (SRP) 400 and willcontain a link 420 to a URL providing information about the clinicaltrial and an on-line registration form.

In step 350 we assign a unique identifier to the subject. This uniqueidentifier will identify the subject within the system 100 and willremain associated with the subject through the trial and any follow-up.In step 360 we store the data for future use as training data. Theidentifier is assigned at the time there is a match between trial andsubject in the system. This triggers the process of notification of HCPand related documentation. An identifier is assigned to the subjectprior to their decision to participate or not in the trial. This isrequired as the decision by the subject happens after consultation withtheir HCP. Information on subjects that did not decide to participate onthe trials is kept for statistical studies. All of this information isstored anonymously, as per HIPAA.

Matching.

Referring now to FIG. 6 we show a flowchart of step 310 of the flowchartof FIG. 3 “Determine query match to target keywords.” In step 311 wereceive the search term from a web service provider such as WebMD.Alternately, we can access the search term from our proprietary website,or other means. In step 312 we parse the search term and remove anyextraneous characters. In parsing the search term 710, we remove anyextraneous characters (such as a question mark) from the term andseparate the words because it may be necessary to use a portion of thesearch term, depending on what the user entered. Then in step 313 wematch the parsed term to a target keyword from a keyword database ofmedical terms. If we find a match, in step 314 we retrieve theidentifier, or key, associated with the keyword. We use that key toretrieve its associated medical condition from a look-up table in step315. Using that listed medical condition, we then search for anyclinical trials for that condition in the clinical trials database 160.

Look-Up Tables.

FIG. 7 provides a simplified illustration of the process of FIG. 6.After parsing the search term 710 we match the parsed term to a targetkeyword 725 from a keyword list 720. The keyword list 720 can be adatabase or a group of databases and can be kept locally or remotely. Inthis example the search term “High Blood Sugar” exactly matches thetarget keyword “high blood sugar” but this will not always be the case.In some cases, only a portion of the search term 710 will match a targetkeyword 725.

Using the target keyword 725 (or in this case an identifier associatedwith the target keyword 725) we look up the related medical condition755 in the look-up table 750. The related medical condition 755 may ormay not be associated with a clinical trial. We determine this bysearching the clinical trials database 160 for the related medicalcondition 755. It should be noted that the target keywords 725 and themedical conditions 755 can be entered with classifiers from the ICD ICD(“International Statistical Classification of Diseases and RelatedHealth Problems”) and DSM (“Diagnostic and Statistical Manual of MentalDisorders”). If the clinical trials database 160 yields a result that isdeemed to be a good match for the subject, the information is sent tothe subject's HCP 780 who will then communicate the subject's decisionand provide any necessary feedback.

FIG. 8 shows an example of using look-up tables to match patientattributes with a patient and then match that patient to a clinicaltrial. We show a look-up table 810 for patients and a look-up table 820for patient attributes. A look-up table 830 for matches has theinformation for both the patient and the clinical trial.

The Simulator.

FIG. 9 shows a simplified diagram of how the feedback loop 190 operateswith respect to the simulator 920. After a clinical trial it isimportant to acquire as much feedback as possible, from both the HCP andthe subject. We implement a ‘Practice Web site’ as a simulator 920. Thissimulator 920 allows the designer, administrator, or analyzer of aclinical trial to model questions, hypothesis, and conditions before,during, or after the trial. This is done by leveraging the existingdatabase of information in the system as a baseline where variables canbe changed, greater precision can be sought, or contextual mapping toprevious or related trials can be performed. This provides a level ofintelligence not available through current paper models ornon-integrated electronic systems.

Within this simulator 920, we can adjust all of the filteringcriteria/filters 922 to match either the pre-defined criteria from aclinical trial or use our analytics capabilities to provide greaterinsight beyond what is requested. This provides greater depth, inclusionof a greater number of weighted variables, and a new dimension ofanalysis.

For instance; we can adjust age, gender, and location with finer grainmodeling against demographic studies and their correlation to diseasetypes. We can validate clinical trial assumptions before the trial isdeployed by matching to our existing database 160. We can validateresults from trials based on our historical database comparisons andidentify inconsistencies and areas for further analysis. The concepthere is that we can iterate on a filtering scheme and reprocess theinformation, looking for more unique trends. This changes the dynamicsfrom coarse to fine grade analysis, thereby providing higher value(precision) to the selection process and insight to the user of thesystem.

From these weighted metrics we further refine the selections by applyinga filter 922 to the selections. The data used in the learning process ismetadata (the data about the data). From our filtering methods weextract trends; these can be used to baseline given criteria. These arefurther used to define a higher level of insight than one-dimensionaldemographics. With every clinical trial, the system learns by capturingsuccess and failures of data. For instance: subjects with a givengender/age group were more likely to respond to trials with a givencriteria. This can further be enhanced by capturing direct informationfrom the subjects about their feedback on process, interests, andrecommendations. With this information, a greater learning dimension isadded.

A survey 910 is a useful way to gather feedback. The feedback is enteredinto the learning database 950 which outputs training data 915. Thistraining data 915 is fed into the simulator 920 which performs simulatedtrial runs 925. The simulator 920 is any computer device or group ofnetworked computing devices that is configured according to anembodiment of the present invention. Data from the simulated trial runs925 is returned to the simulator 920 and also stored in the database950.

FIG. 10 shows a survey generator 1050 which is also part of the feedbackloop 190. The survey generator 1050 prepares the surveys 910 which areprovided to trial subjects, trial coordinators, and health careproviders. Feedback from the survey 910 is stored in the learningdatabase 950 so that the process can be continually improved. Thesurveys 910 can be customized with the data collected from theparticipants.

FIG. 11 shows a simplified depiction of an on-line registration form1100 for a subject interested in a clinical trial. This on-line form1100 can be presented as a site accessed from the ad 410 shown in FIG. 4that is presented on a search results page. The registration form 1100at a minimum must collect the subject's contact information (emailaddress, phone, address) for further follow-up, if necessary. Thesubject provides this information and then receives a confirmation. Anyrequired follow-up can be done automatically from within the system.

Monetization.

It is contemplated that clinical trial organizations, pharmaceuticalcompanies, medical product companies, and/or health care providers arewilling to pay for a completely automated system to match subjects withclinical trials. The matching service could be provided as asubscription service (monthly fee, yearly fee), or on a “per hit” basis,meaning that the company would pay only in the event of a successfulmatch.

It is further contemplated within the spirit and scope of the inventionthat a charge for providing and monitoring registrations could be billedseparately or included in a subscription service. Moreover, wecontemplate that the survey and feedback processing can also be aseparate charge or provided with a subscription service.

What we have discussed here is a system that can be completelyautomated, from an initial contact with a subject, through completion oftrial and post-trial follow-up.

FIG. 5 Hardware Embodiment.

Referring now to FIG. 5, there is provided a simplified pictorialillustration of an information processing system 500 for identifyingsubjects for clinical trials, in which the present invention may beimplemented. For purposes of this invention, computer system 500 mayrepresent any type of computer, information processing system or otherprogrammable electronic device, including a client computer, a servercomputer, a portable computer, an embedded controller, a personaldigital assistant, and so on. The computer system 500 may be astand-alone device or networked into a larger system. Computer system500, illustrated for exemplary purposes as a networked computing device,is in communication with other networked computing devices (not shown)via network 510. As will be appreciated by those of ordinary skill inthe art, network 510 may be embodied using conventional networkingtechnologies and may include one or more of the following: local areanetworks, wide area networks, intranets, public Internet and the like.

In general, the routines which are executed when implementing theseembodiments, whether implemented as part of an operating system or aspecific application, component, program, object, module or sequence ofinstructions, will be referred to herein as computer programs, or simplyprograms. The computer programs typically comprise one or moreinstructions that are resident at various times in various memory andstorage devices in an information processing or handling system such asa computer, and that, when read and executed by one or more processors,cause that system to perform the steps necessary to execute steps orelements embodying the various aspects of the invention.

Throughout the description herein, an embodiment of the invention isillustrated with aspects of the invention embodied solely on computersystem 500. As will be appreciated by those of ordinary skill in theart, aspects of the invention may be distributed amongst one or morenetworked computing devices which interact with computer system 500 viaone or more data networks such as, for example, network 510. However,for ease of understanding, aspects of the invention have been embodiedin a single computing device—computer system 500.

Computer system 500 includes processing device 502 which communicateswith an input/output subsystem 506, memory 504, storage 510 and network590. The processor device 502 is operably coupled with a communicationinfrastructure 522 (e.g., a communications bus, cross-over bar, ornetwork). The processor device 502 may be a general or special purposemicroprocessor operating under control of computer program instructions532 executed from memory 504 on program data 534 such as patient data110. The processor 502 may include a number of special purposesub-processors such as a comparator engine and filter, eachsub-processor for executing particular portions of the computer programinstructions. Each sub-processor may be a separate circuit able tooperate substantially in parallel with the other sub-processors.

The memory 504 may be partitioned or otherwise mapped to reflect theboundaries of the various memory subcomponents. Memory 504 may includeboth volatile and persistent memory for the storage of: operationalinstructions 532 for execution by CPU 502, data registers, applicationstorage and the like. Memory 504 preferably includes a combination ofrandom access memory (RAM), read only memory (ROM) and persistent memorysuch as that provided by a hard disk drive 518. The computerinstructions/applications that are stored in memory 504 are executed byprocessor 502. The computer instructions/applications 532 and programdata 534 can also be stored in hard disk drive 518 for execution byprocessor device 502.

Those skilled in the art will appreciate that the functionalityimplemented within the blocks illustrated in the diagram may beimplemented as separate components or the functionality of several orall of the blocks may be implemented within a single component. Forexample, the functionality for the filter may be included in the samecomponent as the comparator. The I/O subsystem 506 may comprise variousend user interfaces such as a display, a keyboards, and a mouse. The I/Osubsystem 506 may further comprise a connection to a network 590 such asa local-area network (LAN) or wide-area network (WAN) such as theInternet.

The computer system 500 may also include a removable storage drive 519,representing a CD-ROM drive, a magnetic tape drive, an optical diskdrive, and the like. The removable storage drive 519 reads from and/orwrites to a removable storage unit 520 in a manner well known to thosehaving ordinary skill in the art. Removable storage unit 520, representsa floppy disk, a compact disc, magnetic tape, optical disk, CD-ROM,DVD-ROM, etc. which is read by and written to by removable storage drive519. As will be appreciated, the removable storage unit 520 includes anon-transitory computer readable medium having stored therein computersoftware and/or data.

The computer system 500 may also include a communications interface 512.Communications interface 512 allows software and data to be transferredbetween the computer system and external devices. Examples ofcommunications interface 512 may include a modem, a network interface(such as an Ethernet card), a communications port, a PCMCIA slot andcard, etc. Software and data transferred via communications interface512 are in the form of signals which may be, for example, electronic,electromagnetic, optical, or other signals capable of being received bycommunications interface 512.

In this document, the terms “computer program medium,” “computer usablemedium,” and “computer readable medium” are used to generally refer toboth transitory and non-transitory media such as main memory 504,removable storage drive 520, a hard disk installed in hard disk drive518, and signals. These computer program products are means forproviding software to the computer system 500. The computer readablemedium 520 allows the computer system 500 to read data, instructions,messages or message packets, and other computer readable informationfrom the computer readable medium 520.

Therefore, while there has been described what is presently consideredto be the preferred embodiment, it will understood by those skilled inthe art that other modifications can be made within the spirit of theinvention. The above description(s) of embodiment(s) is not intended tobe exhaustive or limiting in scope. The embodiment(s), as described,were chosen in order to explain the principles of the invention, showits practical application, and enable those with ordinary skill in theart to understand how to make and use the invention. It should beunderstood that the invention is not limited to the embodiment(s)described above, but rather should be interpreted within the fullmeaning and scope of the appended claims.

We claim:
 1. A method of automatically matching a patient with anavailable clinical trial, said method comprising: using an input/outputinterface device performing: collecting patient data associated with thesubject; and collecting clinical trial data from multiple sources; usinga processor device operably coupled to the input/output interface, saidprocessor device performing: matching the subject to a clinical trialscheduled in a location accessible to said subject; and using theinput/output interface to perform: notifying a health care providerassociated with the subject about the clinical trial; and receiving aresponse.
 2. The method of claim 1 wherein collecting the patient datacomprises receiving voice data from the subject transmitted through acall center.
 3. The method of claim 1 wherein collecting the patientdata comprises receiving an on-line inquiry from a web site.
 4. Themethod of claim 1 wherein notifying the health care provider comprisestransmitting an electronic message.
 5. The method of claim 1 furthercomprising: receiving an indication that the subject matched to theclinical trial agrees to participate in said clinical trial; assigning aunique identifier to the subject; receiving information from thesubject; and storing the subject information for use as training data.6. The method of claim 1 further comprising: receiving an indicationthat the subject matched to the clinical trial subject declines toparticipate in the clinical trial; soliciting a reason why the subjectdeclines to participate; storing the reason for refusal; and inputtingthe reason for refusal as training data in a learning tool.
 7. Themethod of claim 1 wherein matching the subject to the clinical trialcomprises: assigning weighted values to subject parameters; assigningweighted values to clinical trial parameters; running an algorithm togenerate a primary selection of matched subject/trial pairs; andapplying a filter to further refine the primary selection to generate asecondary selection.
 8. The method of claim 1 further comprising:providing a survey to at least one of: the subject, the health careprovider, and an organization conducting the clinical trial; receivingfeedback from the survey; and inputting the survey feedback as trainingdata in a learning tool.
 9. A method for soliciting a subject for aclinical trial, said method comprising steps of: receiving ahealth-related search term provided by the subject; determining alocation of the subject; matching the health-related search term to atarget keyword from a data store; mapping the target keyword to amedical condition under consideration for a clinical trial; determiningthat the clinical trial for the medical condition is scheduled in alocation accessible to the subject; and serving an announcement to thesubject with a link to a site with information about the clinical trial.10. The method of claim 9 wherein serving the announcement comprisesserving an on-line advertisement.
 11. The method of claim 9 furthercomprising: receiving a selection of the link to the site withinformation about the clinical trial; presenting the site withinformation about the clinical trial, wherein said site comprises aninterface for on-line registration for the clinical trial.
 12. Themethod of claim 9 wherein receiving the health-related search termcomprises receiving a search query input by the subject through a webbrowser.
 13. The method of claim 9 wherein serving the announcementcomprises serving a dialog box with a voice data link to a call center.14. The method of claim 10 further comprising serving the on-lineadvertisement on a search results page.
 15. A distributed computingsystem for identifying a subject for a clinical trial, said systemcomprising: a memory with computer-executable instructions storedtherein, said instructions causing a computing device to perform:collecting patient data associated with the subject; collecting clinicaltrial data from multiple sources; matching the subject to a clinicaltrial scheduled in a location accessible to said subject; notifying ahealth care provider associated with the subject about the clinicaltrial; and receiving a response; and a processor device, operablycoupled to the memory, for executing the instructions.
 16. Thedistributed computing system of claim 15 further comprising: a learningdatabase for storing data about matched subject/trial pairs as trainingdata; and a simulator using the training data as input.
 17. Thedistributed computing system of claim 15 wherein the patient datacomprises: subject health information and subject health concerns. 18.The distributed computing system of claim 17 wherein the patient datafurther comprises at least one of: lifestyle data, genomics data,environmental data, and demographic data.
 19. The distributed computingsystem of claim 15 wherein the patient data comprises location data usedto match the subject with a locally available clinical trial.
 20. Thedistributed computing system of claim 15 further comprising: a surveygenerator producing customized surveys presented to at least one of:trial subjects, trial coordinators, and health care providers.